Estimating Population Density Without Contravening Citizen’s Privacy: Warsaw Use Case

Luckner, Marcin; Krzeminska, Izabella; Wawrzyniak, Piotr; Legierski, Jarosław

doi:10.1109/tsmc.2021.3096754

Cited by 3 publications

(1 citation statement)

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“…The congestion map obtained by this method has a low resolution and cannot help vehicle dispatchers to accurately locate the crowd. Luckner et al [44] proposed that the primary task of estimating the level of crowd congestion is to correctly estimate the number of crowds. This has been reported in the literature on the relationship between local population density estimates and crowd counts, such as by Khan et al [45] where finding the population size of an entire region to provide hazard warnings is not reliable because the hazard may only occur locally.…”

Section: Related Workmentioning

confidence: 99%

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Ren

2022

Journal of Sensors

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Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.

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Section: Related Workmentioning

confidence: 99%